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» Learning from Highly Structured Data by Decomposition
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KDD
2005
ACM
149views Data Mining» more  KDD 2005»
15 years 3 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
MICCAI
2000
Springer
15 years 1 months ago
Small Sample Size Learning for Shape Analysis of Anatomical Structures
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
GIS
2004
ACM
15 years 10 months ago
Object-relational management of complex geographical objects
Modern database applications including computer-aided design, multimedia information systems, medical imaging, molecular biology, or geographical information systems impose new re...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...
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MICCAI
2007
Springer
15 years 10 months ago
Hyperspherical von Mises-Fisher Mixture (HvMF) Modelling of High Angular Resolution Diffusion MRI
A mapping of unit vectors onto a 5D hypersphere is used to model and partition ODFs from HARDI data. This mapping has a number of useful and interesting properties and we make a li...
Abhir Bhalerao, Carl-Fredrik Westin
IV
2003
IEEE
98views Visualization» more  IV 2003»
15 years 2 months ago
The Stardinates - Visualizing Highly Structured Data
The Stardinates are a novel interactive Information Visualization (InfoVis) technique which aims at visualizing highly structured data. They represent some Gestalt principles very...
Monika Lanzenberger, Silvia Miksch, Margit Pohl